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Olecranon bone age assessment in puberty using a lateral elbow radiograph and a deep-learning model

Authors
 Gayoung Choi  ;  Sungwon Ham  ;  Bo-Kyung Je  ;  Young-Jun Rhie  ;  Kyung-Sik Ahn  ;  Euddeum Shim  ;  Mi-Jung Lee 
Citation
 EUROPEAN RADIOLOGY, Vol.34(10) : 6396-6406, 2024-10 
Journal Title
EUROPEAN RADIOLOGY
ISSN
 0938-7994 
Issue Date
2024-10
MeSH
Adolescent ; Age Determination by Skeleton* / methods ; Child ; Deep Learning* ; Elbow / diagnostic imaging ; Elbow Joint / diagnostic imaging ; Female ; Humans ; Male ; Olecranon Process* / diagnostic imaging ; Puberty / physiology ; Radiography / methods ; Reproducibility of Results ; Retrospective Studies
Keywords
Age determination by skeleton ; Artificial intelligence ; Deep learning ; Olecranon process ; Puberty
Abstract
Objectives: To improve pubertal bone age (BA) evaluation by developing a precise and practical elbow BA classification using the olecranon, and a deep-learning AI model.

Materials and methods: Lateral elbow radiographs taken for BA evaluation in children under 18 years were collected from January 2020 to June 2022, retrospectively. A novel classification and the olecranon BA were established based on the morphological changes in the olecranon ossification process during puberty. The olecranon BA was compared with other elbow and hand BA methods, using intraclass correlation coefficients (ICCs), and a deep-learning AI model was developed.

Results: A total of 3508 lateral elbow radiographs (mean age 9.8 ± 1.8 years) were collected. The olecranon BA showed the highest applicability (100%) and interobserver agreement (ICC 0.993) among elbow BA methods. It showed excellent reliability with Sauvegrain (0.967 in girls, 0.969 in boys) and Dimeglio (0.978 in girls, 0.978 in boys) elbow BA methods, as well as Korean standard (KS) hand BA in boys (0.917), and good reliability with KS in girls (0.896) and Greulich-Pyle (GP)/Tanner-Whitehouse (TW)3 (0.835 in girls, 0.895 in boys) hand BA methods. The AI model for olecranon BA showed an accuracy of 0.96 and a specificity of 0.98 with EfficientDet-b4. External validation showed an accuracy of 0.86 and a specificity of 0.91.

Conclusion: The olecranon BA evaluation for puberty, requiring only a lateral elbow radiograph, showed the highest applicability and interobserver agreement, and excellent reliability with other BA evaluation methods, along with a high performance of the AI model.

Clinical relevance statement: This AI model uses a single lateral elbow radiograph to determine bone age for puberty from the olecranon ossification center and can improve pubertal bone age assessment with the highest applicability and excellent reliability compared to previous methods.

Key points: Elbow bone age is valuable for pubertal bone age assessment, but conventional methods have limitations. Olecranon bone age and its AI model showed high performances for pubertal bone age assessment. Olecranon bone age system is practical and accurate while requiring only a single lateral elbow radiograph.
Full Text
https://link.springer.com/article/10.1007/s00330-024-10748-x
DOI
10.1007/s00330-024-10748-x
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Lee, Mi-Jung(이미정) ORCID logo https://orcid.org/0000-0003-3244-9171
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/206550
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